Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes
Publication Type
Journal Article
Publication Date
1-2019
Abstract
In this paper, we propose a privacy-preserving reinforcement learning framework for a patient-centric dynamic treatment regime, which we refer to as Preyer. Using Preyer, a patient-centric treatment strategy can be made spontaneously while preserving the privacy of the patient's current health state and the treatment decision. Specifically, we first design a new storage and computation method to support noninteger processing for multiple encrypted domains. A new secure plaintext length control protocol is also proposed to avoid plaintext overflow after executing secure computation repeatedly. Moreover, we design a new privacy-preserving reinforcement learning framework with experience replay to build the model for secure dynamic treatment policymaking. Furthermore, we prove that Preyer facilitates patient dynamic treatment policymaking without leaking sensitive information to unauthorized parties. We also demonstrate the utility and efficiency of Preyer using simulations and analysis.
Keywords
Cryptography, Diseases, Dynamic Treatment Regime, Experience Replay, Patient-Centric, Privacy, Privacy-Preserving, Protocols, Q-learning, Reinforcement learning, Reinforcement Learning, Cloud computing, Computational modeling
Discipline
Health Information Technology | Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Emerging Topics in Computing
ISSN
2168-6750
Identifier
10.1109/TETC.2019.2896325
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
LIU, Ximeng; DENG, Robert H.; CHOO, Kim-Kwang Raymond; and YANG, Yang.
Privacy-preserving reinforcement learning design for patient-centric dynamic treatment regimes. (2019). IEEE Transactions on Emerging Topics in Computing.
Available at: https://ink.library.smu.edu.sg/sis_research/5073
Additional URL
https://doi.org/10.1109/TETC.2019.2896325